{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.00000000e+00, -9.00000000e-01, -8.00000000e-01, -7.00000000e-01,\n",
       "       -6.00000000e-01, -5.00000000e-01, -4.00000000e-01, -3.00000000e-01,\n",
       "       -2.00000000e-01, -1.00000000e-01, -2.22044605e-16,  1.00000000e-01,\n",
       "        2.00000000e-01,  3.00000000e-01,  4.00000000e-01,  5.00000000e-01,\n",
       "        6.00000000e-01,  7.00000000e-01,  8.00000000e-01,  9.00000000e-01])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(-1,1,0.1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'yaxis'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-8e70b37c5798>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'sinx'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick_right\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'yaxis'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.sin(x)\n",
    "plt.plot(x,y,label='sinx')\n",
    "plt.yaxis.tick_right()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Categorical Plotting')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'apple': 10, 'orange': 15, 'lemon': 5, 'lime': 20}\n",
    "names = list(data.keys())\n",
    "values = list(data.values())\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(9, 3), sharey=True)\n",
    "axs[0].bar(names, values)\n",
    "axs[1].scatter(names, values)\n",
    "axs[2].plot(names, values)\n",
    "fig.suptitle('Categorical Plotting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
